System and method for selectively noise-filtering digital images

ABSTRACT

A system and method for noise filtering digital images is implemented in an automated image enhancement system without significantly reducing the performance of the automated image enhancement system. The system and method trigger a noise filter based on image measurements. If the image measurements indicate that the likelihood of objectionable &#34;noise&#34; in the image is high, a noise filter is triggered. Otherwise the noise filter is not triggered. In this way, only images that are in need of noise filtering are filtered, while other images are processed without the additional performance overhead required by the noise filter.

BACKGROUND OF THE INVENTION

1. Field of Invention

This invention relates to a system and method for improving theappearance of a digital image. More specifically, this invention isdirected to a system and method for efficiently reducing image noise or"speckle" in digital images.

2. Description of Related Art

In the past, copiers or scan-to-print image processing systems weretypically used to reproduce an input image as accurately as possible,i.e., to render a copy. Copies are intentionally rendered as accuratelyas possible, including any flaws present in the original input image.However, as customers become more knowledgeable in their documentreproduction requirements, they recognize that an exact copy is oftennot actually desired. Instead, they desire to obtain the best documentoutput.

Until recently, output image quality of a copier or a scan-to-printsystem was directly related to the input document quality. One commonset of input documents includes photographs. Unfortunately, photographyis an inexact science, and original photographs are often of poorquality. In addition, technology, age and/or image degradationvariations often result in pictures having an unsatisfactory andundesirable appearance. Therefore, a copy giving the best possiblepicture is desired, rather than an exact copy of the original image.

Photography has long dealt with this issue. Analog filters andillumination variations can improve the appearance of pictures in analogphotographic reproduction processes. For example, yellow filters enhancethe appearance of white clouds against a blue sky in black and whiteimages. Furthermore, various electrophotographic devices, includingdigital copiers, can clean up and improve images by adjusting thresholdvalues, image filters and/or background suppression levels. Generally,these methods are manual methods in which a user must select variousimage adjustment operations on an image by image basis. Typically, thecasual user is not skilled enough to perform these operations,especially when the electrophotographic device includes color controls.

Three possible choices are currently available to enhance an image. Thefirst choice is doing nothing. Such a system is a stable system, in thatit does no harm to an image. This is a common reproduction approach.However, the output quality of documents produced by these systems aresometimes not satisfactory to the ultimate customer.

The second choice is to process all images. An improvement can usuallybe made to an image if certain assumptions are made that are accuratefor most cases. In an exceptionally large subset of images, increasingcontrast, sharpness and/or color saturation will improve these images.This reproduction process tends to produce better images. However, theprocess is unstable because, for multi-generation copying, increases incontrast, saturation or sharpness are undesirable and ultimately lead tosevere image degradation. Furthermore, the process may undesirablyoperate on high quality images.

The third choice is an automated image enhancement process whichoperates to vary images which are not perceived as high quality images,but does not operate on images which do not need to be improved.

Many improvements can be made to an image using automated imageenhancement techniques, including exposure adjustment, described in U.S.Pat. No. 5,414,538 to Eschbach, color balance correction, described inU.S. Pat. No. 5,371,615 to Eschbach, and contrast enhancement, describedin U.S. Pat. No. 5,450,502 to Eschbach et al., each herein incorporatedby reference in their entireties.

Generally, these processing methods operate by modifying a set of tonereproduction curves. The output image is achieved by using tonereproduction curves, operating either on the luminescence channelexpressed in LC₁ C₂ coordinates or, preferably, on each channel in acolor density space description of the image in red-green-blue (RGB)coordinates. A method of cascading or serially ordering such processingis described in U.S. Pat. No. 5,347,374 to Fuss et al., hereinincorporated by reference in its entirety.

The automated image enhancement techniques described above attempt toautomatically improve the perceived quality of natural scene images byestimating and appropriately modifying the overall exposure, contrast,color balance, saturation and sharpness of the input image. However, onesource of image degradation, image noise or "speckle", is not addressedby these techniques. Image noise is not addressed by the above automatedimage advancement techniques because a noise filter, including even asimple low-pass filter, takes a considerable amount of time to operateon the input image. This undesirably reduces the overall performance ofthe system. Specifically, a noise filter reduces performance of anautomated image enhancement system by as much as 50%.

SUMMARY OF THE INVENTION

This invention provides a system and method for noise-filtering digitalimages implemented in automated image enhancement systems withoutsignificantly reducing the performance of the automated imageenhancement system.

The system and method of this invention trigger a noise filter based onthe results of image measurements. If the image measurements indicatethat the likelihood of objectionable "noise" in the image is high, anoise filter is triggered. Otherwise, the noise filter is not triggered.In this way, only images that are in need of noise filtering arefiltered, while other images are processed without the additionalperformance overhead required by the noise filter.

In a preferred embodiment, the system and method trigger a noise filteronly if the image is darker than a predetermined threshold, and if animage's contrast is below a predetermined threshold.

In the preferred embodiment, the noise filter comprises a singlemodified sigma filter that reduces both "random" noise and "shot" noise.Thus, a user does not have to monitor the type of noise present in theimage and manually select between a random noise filter and a shot noisefilter.

These and other features and advantages of this invention are describedin or are apparent from the following detailed description of thepreferred embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

The preferred embodiments of this invention will be described in detail,with reference to the following figures, wherein:

FIG. 1 is a block diagram of a system employing the present invention;

FIG. 2 is a block diagram of a preferred embodiment of the selectivenoise-filtering system of this invention;

FIG. 3 is a block diagram of a preferred image processor used in theselective noise-filtering system of this invention;

FIG. 4 shows an example image;

FIG. 5 shows a histogram derived from the image of FIG. 4;

FIG. 6 shows histograms for corresponding local areas of the image ofFIG. 4;

FIG. 7 is a flowchart of a preferred control routine for the selectivenoise-filtering system of this invention;

FIG. 8 is a flow chart of a preferred control routine for the contrastdetermination step of FIG. 7;

FIG. 9 is a flow chart of a preferred control routine for the exposuredetermination step of FIG. 7; and

FIG. 10 is an image corresponding to FIG. 5, where the methods of thisinvention have been used to filter "random" noise and "shot" noise.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

FIG. 1 illustrates a scan-to-print system 100 incorporating thisinvention. The system 100 includes an image digitizer, such as a scanner200, which may be a black and white or color scanner which producesimage signals defined in either RGB space for color images or densityspace for black and white images. The images of concern are pictorial innature, i.e., they represent natural scenes. While certain computergenerated imagery may qualify as representing natural scenes, the classof images contemplated as inputs to the system 100 are predominatelyscanned photographs.

The images themselves are defined in terms of pixels, wherein each pixelis an electrical or electronic signal with a digital gray value whichvaries between a white level (in this example system, a maximum value)and a black level (in this example system, a minimum value). In thesystem 100, which uses 8-bit pixels, 256 levels of gray are available.Pixels are also identified in terms of position, i.e., a pixel defines aunique area within the image identified by its position in a scanline,and the scanline's position in the image. Therefore, color isrepresented by an 8-bit gray pixel for each color separation of theimage. Each 8-bit gray pixel defines the color in one separation. Thecolor separations together form the color pixel. In most cases, thereare three color separations.

The output of the scanner 200 is directed to an automated imageenhancement system 300. The automated image enhancement system 300includes a segmentation system which identifies a type of image within adocument, and optionally a descreening system. The system 100 operateson continuous tone natural scene images. Thus, to operate on halftoneimages, the halftone images need to be descreened to recover theoriginal continuous tone image. The output of the automated imageenhancement system 300 is ultimately directed to a printer/display 400,which can be a printer, a CRT, or a similar output or display device.These devices may be laser printers, ink jet printers, LED displays orCRT displays. The printer/display 400 must be capable of representinggray pictorial images. The printer/display 400 preferably accomplishesthis with gray printing and/or pseudo gray printing.

To derive the data needed for operating the system 100, a document maybe prescanned by placing it on a copying platen and scanning it usingthe electro-optical system of the scanner 200 to produce a signalrepresenting the document image. Alternatively, the image may bedirected to the automated image enhancement system 300 from a memory500, having previously been scanned or derived by some other system, inwhich case the received image is sampled as required.

The image need not be sampled at the ultimate resolution of the system.In practice, the automated image enhancement system 300 can use arelatively small number of pixels representative of and dispersedthrough the entire image to accurately represent the image. In thepreferred embodiment, the system 100 uses a 512×512 block of pixelsderived from the image. This selection improves the speed at which theimage enhancement system 300 processes the pictorial images. Sampling atcommon image resolutions does not significantly improve the output ofthe image enhancement system 300, but does dramatically increase theprocessing time required by the image enhancement system 300. However,it is not necessary to undersample the image.

The natural scene images defined in terms of RGB space are initiallydirected to a color space converter 600, which converts the RGB pixelvalues to a selected color space for enhancement processing. The outputof color space converter 600 is processed by the automated imageenhancement device 300. The automated image enhancement device 300produces a signal that drives the tone reproduction curve controller.The tone reproduction curve controller 700 transmits the processed datato an optional output buffer 800, for subsequent transfer to theprinter/display device 400. It should be appreciated that the TRCcontroller 700 may work separately or integrally with the TRC controllerthat is commonly used to adjust the device independent data stream tothe device dependent data used for printing or display.

The initial color image data signal received from the scanner 200 isassumed to be in RGB space. This RGB signal is converted to a luminancespace signal (YC₁ C₂) by the color space converter 600. However, it ispossible that the image will already be a luminance space signal, asconverting RGB values to luminance/chrominance space is commonly usedfor other image processing. YC₁ C₂ space is a useful space in which themethods of this invention are preferably performed. YES space is onepossible embodiment of YC₁ C₂ space. Other color space embodiments maybe used. However, the color space used must have a component whichrelates to the human visual perception of lightness or darkness, such asthe Y-component of YES color space. The system and method of the presentinvention will be described using the YES color space.

The automated image enhancement device 300 enhances the image data. Aspart of the enhancement function, the input image's exposure andcontrast may be changed. For visually dark images, the input image datais "lightened" by an exposure control mechanism, as described in theincorporated '538 patent. One common artifact of "lightening" darkimages is the associated amplification of input image noise. For strongexposure changes, this noise amplification becomes objectionable.

Another optional process performed by the automated image enhancementdevice 300 is the adjustment of image contrast. For visually lowcontrast images, the input image data is "contrast enhanced" by acontrast control mechanism, as described in the incorporated '502patent. One common artifact of "contrast enhancing" low contrast imagesis the associated amplification of input image noise. For strongcontrast changes, this noise amplification becomes objectionable inslowly varying regions.

FIG. 2 illustrates a preferred embodiment of the selectivenoise-filtering system 900 of this invention, which is preferablyimplemented in the automated image enhancement system 300. The imagedata is input to an image processor 910 over a signal line 911. Theimage processor 910 outputs the image data to a noise filter 920 over asignal line 912, or out of the selective noise-filtering system 900 overa signal line 913. The image processor 910 outputs control signals overa signal line 914 to the noise filter 920. The noise filter 920 outputsthe noise-filtered image over a signal line 921.

In operation, the image processor 910 determines the likelihood that theimage data is "noisy" by evaluating predetermined image noiseindicators, as will be described in more detailed below. If the imageprocessor 910 determines that image data is likely noisy, it sends theimage data to the noise filter 920 for noise filtering. Otherwise, itdirects the non-filtered image data out of the noise-filtering system900.

In a first preferred embodiment, the image processor 910 sends the imagedata to the noise filter 920 only if the image is darker than a firstpredetermined threshold and the image's contrast is below a secondpredetermined threshold. The noise filter 920 preferably comprises asingle modified sigma filter, such as the filter described in Lee,"Digital Image Smoothing and the Sigma Filter," Computer Vision,Graphics, and Image Processing, vol. 24, pp. 255-269, 1983. The modifiedsigma filter 920 reduces both random noise and shot a user does noise.Thus, a user does not have to monitor the type of noise present in theimage to manually select between a random noise filter and a shot noisefilter.

FIG. 3 illustrates a preferred embodiment for the image processor 910used in this invention. The image processor 910 includes a memory 930, ahistogram generator 940, a contrast analyzer 950, an exposure analyzer960 and a controller 970.

The image data is input to the memory 930 over the signal line 911. Theimage data is output from the memory 930 to the histogram generator 940and the controller 970 over signal lines 931 and 932, respectively. Thehistogram generator 940 generates histograms that are output to thecontrast analyzer 950 and the exposure analyzer 960 over signal lines941 and 942, respectively. The contrast analyzer 950 outputs imagecontrast indicators to the controller 970 over a signal line 951, whilethe exposure analyzer 960 outputs image exposure indicators to thecontroller 970 over a signal line 961. The controller 970 outputscontrol signals to the memory 930, the histogram generator 940, thecontrast analyzer 950 and the exposure analyzer 960 over signal lines971, 972, 973 and 974, respectively.

FIG. 4 shows an unfiltered input image having shot and random noise. Thehistogram generator 940 generates a global histogram of the luminance orY-component of the image of FIG. 4. The global histogram shown in FIG. 5shows the populations of pixels at each luminance value possible in theimage. The global histogram of FIG. 5 corresponds to the entire image ofFIG. 4. If multiple bits are used for each color separation, such as8-bits, the luminance values will be between 0 and 255.

In addition to generating a global histogram of the entire image, thehistogram generator 940 also divides the image into a set of localareas, not necessarily identical in size, or ordered in any fashion, andgenerates a histogram for each local area. FIG. 7 shows the localhistograms generated for corresponding local areas of the image of FIG.4. The histogram generator 940 generates the local area histograms forthe image because visual contrast is not a global phenomenon. Forexample, some local areas may not exhibit full dynamic range, whileothers do. Also, the analysis of local areas gives some indication ofthe relative importance of individual image areas. In addition, largebackground areas, which are irrelevant to contrast adjustment, tend toskew the global histogram in a manner that makes contrast determinationdifficult. The histogram generator 940 reduces the influence of theselarge background areas by generating local histograms.

The histogram generator 940 outputs the global and local histogram tothe contrast analyzer 950. The contrast analyzer 950 calculates a globalcontrast variance value V_(G) from the global histogram and local areacontrast variance values V_(Li) from the local histograms. The contrastvariance values are calculated using the techniques disclosed in theincorporated '502 patent. Briefly, the contrast analyzer 950 calculatesthe contrast variance values V_(G) and V_(Li) by comparing the globaland local histograms to a flat reference histogram. A flat referencehistogram is a reference signal which provides a uniform number of pixelcounts for each density or luminance value possible within the image.The global and local histograms are compared to this flat referencehistogram to give a global and local measure of contrast in the form ofa variance value. Higher contrast variance values correspond to lowerimage contrast, while lower contrast indicator values correspond tolower image contrast. The contrast analyzer 950 outputs the contrastvariance values V_(G) and V_(Li) to the controller 970.

The exposure analyzer 960 receives the global histogram values fromhistogram generator 940 and calculates at least one exposure indicatorvalue, and preferably two exposure indicator values γ₁₀₀ and γ₉₀, usingthe techniques disclosed in the incorporated '538 patent. γ₁₀₀ is anexposure indicator value calculated by the exposure analyzer 960 usingthe entire global histogram for the image. To ensure that the results ofthe exposure calculation do not rely on image aberrations at the ends ofthe dynamic range, the exposure analyzer 960 also calculates theexposure indicator value λ₉₀ using the middle 90% of the globalhistogram data for the image. Higher exposure indicator valuescorrespond to a lighter image, while lower exposure indicator valuescorrespond to a darker image. The exposure indicator values γ₁₀₀ and γ₉₀are output to the controller 970.

The controller 970 determines the likelihood that image is "noisy" bycomparing the contrast variance values V_(G), V_(Li) and the exposureindicator values γ₁₀₀ and γ₉₀ to predetermined threshold values. If thecontroller 970 determines that the image is noisy, it sends the imagedata to the noise filter 920. Otherwise, it outputs the image data outof the selective noise-filtering system 900.

FIG. 7 shows a preferred control routine for the conditionalnoise-filtering system 900 of this invention. The routine starts at stepS900 and proceeds to step S920, where the controller 970 determines ifthe image contrast is below a first predetermined threshold. If theimage contrast is below the first predetermined threshold, at step S940the controller 970 outputs the unfiltered image. Otherwise, controlcontinues to step S960.

In step S960, the controller 970 determines if the image exposure isbelow a second predetermined threshold.

If the image exposure is not below the second predetermined threshold,control jumps to step S940, where the controller 970 outputs theunfiltered image. Otherwise, control continues to step S980.

In step S980, the controller 970 filters the image data for both randomnoise and shot noise, preferably using the methods described in Lee. Thecontrol system then continues to step S1000. In step S1000, the controlroutine stops.

It should be appreciated that, although the preferred control routine ofFIG. 7 evaluates both the image's contrast (at step S920) and theimage's exposure (at step S960) to determine the likelihood that noiseis present in the image, the control routine could evaluate only theimage's contrast or only the image's exposure to make the noisedetermination.

If only the image's contrast is evaluated, step S960 would be eliminatedfrom the control routine of FIG. 7, and control would jump to step S980if the controller 970 determines that the image's contrast is belowthreshold. If only the image's exposure is evaluated, step S920 would beeliminated from the control routine of FIG. 7, and control would proceeddirectly to step S960 from step S900.

FIG. 8 shows a preferred control routine for determining if the imagecontrast is below the first predetermined threshold of step S920.

The control routine starts at step S921 and continues to step S922,where the control system determines if the global contrast variancevalue V_(G) is greater than the first threshold value Th1. In thepreferred embodiment, the threshold value Th1 is preferably set to 50.However, the threshold value Th1 may be adjusted up or down in order toachieve a desired balance between system performance and noisereduction.

If V_(G) is greater than Th1, the controller 970 determines that theimage's contrast is below threshold (indicating that the image is likelynoisy), and the control routine jumps to step S923, which returns thecontrol routine to step S930. Otherwise, the routine continues to stepS924. In step S923, a counter i and a counter c are set to 1. Thecounter i indicates which local area contrast variance value is to becompared against the second threshold Th2. In step S925, the controller970 determines if a first local area contrast variance value V_(L1) isgreater than the threshold value Th2. In the preferred embodiment, thethreshold value Th2 is preferably set to 100. However, the thresholdvalue Th2 may be adjusted up or down in order to achieve a desiredbalance between system performance and noise reduction.

If V_(L1) is greater than Th2, the control routine jumps to step S926.Otherwise, the control routine continues to step S927. In step S926, thecontrol routine increments the counter c by 1. Control then continues tostep S927.

At step S927, the control system increments counter i by 1, and controlcontinues to step S928. At step S928, the control system determines if iis equal to N, where N is the total number of local area contrastvariance values V_(Li). If i is not equal to N, the control system jumpsback to step S925, where it compares the second contrast variance valueV_(L2) to threshold value Th2. If i is equal to N, the control systemdetermines that all local area contrast variance values have beencompared to threshold value Th2, and control continues to step S929.

At step S929, the control system determines if counter c is greater thanthreshold value Th3, where c represents the total number of local areacontrast variance values that are greater than threshold value Th2. Inthe preferred embodiment, the threshold value Th3 is preferably set to0.25M, where M is the total number of local area histograms. As shown inFIG. 6, 16 local area histograms were generated for the image of FIG. 4.For this example, Th3 is preferably set to 4 (0.25*16). However, thethreshold value Th3 may be adjusted up or down in order to achieve adesired balance between system performance and noise reduction.

If c is greater than Th3, the controller 970 determines that the image'scontrast is below threshold (indicating that the image is likely noisy),and control continues to step S930. Otherwise, control continues to stepS931. At step S930, the control system returns the control routine tostep S960. At step S931, the control system returns the control routineto step S940.

Although the preferred control routine of FIG. 8 evaluates both theglobal contrast variance values V_(G) and the local area contrastvariance values V_(Li) to determine if the image contrast is below thepredetermined threshold, this is not required. The control routine couldevaluate only the global contrast variance value V_(G), or only thelocal area contrast variance values V_(Li), and still fall within thescope of the methods of this invention.

FIG. 9 shows a preferred control routine for determining if the imageexposure is below the second predetermined threshold of step S960.

In step S961, the controller 970 determines if the exposure indicatorγ₁₀₀ is less than the second predetermined threshold value Th4. In thepreferred embodiment, the threshold value Th4 is preferably set to 2.However, the threshold value Th4 may be adjusted up or down in order toachieve a desired balance between system performance and noisereduction.

If γ₁₀₀ is less than Th4, control continues to step S962. Otherwise,control jumps to step S964. At step S962, the controller 970 determinesif the exposure indicator γ₉₀ is less than the threshold value Th4. Ifγ₉₀ is less than Th4, the controller 970 determines that the image'sexposure is below threshold (indicating that the image is likely noisy),and control continues to step S963. Otherwise control jumps to stepS964.

At step S963, the control system returns the control routine to stepS980. At step S964, the control system returns the control routine tostep S940.

Although the preferred control routine of FIG. 9 evaluates two exposureindicators λ₁₀₀ and λ₉₀ to determine if the image exposure is below thepredetermined threshold, this is not required. For example, the controlroutine could evaluate only one exposure indicator and still fall withinthe scope of the methods of this invention.

The conditional noise-filtering system 900 is preferably implemented ona programmed general purpose computer. However, it can also beimplemented on a special purpose computer, a programmed microprocessoror a microcontroller and peripheral integrated circuit elements, an ASICor other integrated circuit, a hardwired electronic or logic circuitsuch as a discreet element circuit, a programmable logic device such asa POD, POA or PAL, or the like. In general, any device on which a finitestate machine capable of implementing the flow charts shown in FIGS. 7,8 and 9 can be used to implement the conditional noise-filtering system900 of this invention.

FIG. 10 shows an image corresponding to FIG. 4, where both random noiseand shot noise are filtered using the conditional noise-filteringmethods of this invention. As discussed above, the threshold valuesTh1-Th4 are preferably adjusted so that only those images in need ofnoise filtering are filtered, while other images are processed withoutthe additional performance overhead required by the noise filter.

While this invention has been described in conjunction with the specificembodiments outlined above, it is evident that many alternatives,modifications and variations will be apparent to those skilled in theart. As explained above, although the preferred embodiments of thesystem and method determine the likelihood that noise is present in theimage based on the image's contrast and exposure, the system and methodcan make a noise determination based only on the image's contrast oronly on the image's exposure. Accordingly, the preferred embodiments ofthe invention as set forth above are intended to be illustrative notlimiting. Various changes may be made without departing from the spiritand scope of the invention as defined in the following claims.

What is claimed is:
 1. A selective noise filtering system for selectively filtering images, comprising:a noise filter; an image processor that receives an original image, determines if at least one contrast variance value representative of noise in the original image is above a corresponding predetermined threshold, and outputs the original image to the noise filter if the at least one contrast variance value is above the corresponding predetermined threshold, comprising:a histogram generator that generates from the image at least one of a global histogram and at least one local area histogram, a contrast analyzer that generates the at least one contrast variance value based on the at least one of the a global histogram and the at least one local area histogram, and a controller that controls the histogram generator and the contrast analyzer.
 2. A selective noise filtering system for selectively filtering images, comprising:a noise filter; an image processor that receives an original image, determines if at least one exposure indicator representative of noise in the original image is above a corresponding predetermined threshold, and outputs the original image to the noise filter if the at least one exposure indicator is above the corresponding predetermined threshold, comprising:a histogram generator that generates from the image at least one of a global histogram and at least one local area histogram, an exposure analyzer that generates at least one exposure indicator based on the at least one of a global histogram and the at least one local area histogram, and a controller that controls the histogram generator and the exposure analyzer.
 3. A selective noise filtering system for selectively filtering images, comprising:a noise filter; and an image processor that receives an original image, determines if at least one of at least one contrast variance value and at least one exposure indicator representative of noise in the original image is above a corresponding predetermined threshold, and outputs the original image to the noise filter if the at least one of at least one contrast variance value and at least one exposure indicator is above the corresponding predetermined threshold, comprising:a histogram generator that generates from the image at least one of a global histogram and at least one local area histogram, a contrast analyzer that generates at least one contrast variance value based on the at least one of a global histogram and the at least one local area histogram, an exposure analyzer that generates at least one exposure indicator based on the at least one of a global histogram and the at least one local area histogram, and a controller that controls the histogram generator, the contrast analyzer and the exposure analyzer.
 4. The system of claim 3, wherein the exposure analyzer generates two exposure indicators.
 5. The system of claim 4, wherein the contrast analyzer generates a global contrast variance value based on the global histogram, and at least one local area contrast variance value based on the at least one local area histogram.
 6. The system of claim 5, wherein the controller outputs the image to the noise filter when:

    V.sub.G <Th1,

and

    γ.sub.100 >Th4

wherein: V_(G) is the global contrast variance value; γ₁₀₀ is an exposure indicator; Th1 is a first predetermined contrast threshold value; and Th4 is second predetermined exposure threshold value.
 7. The system of claim 5, wherein the controller outputs the image to the noise filter when:

    V.sub.G >Th1;

    γ.sub.100 <Th4;

and

    γ.sub.90 <Th4

wherein: V_(G) is the global contrast variance value; γ₁₀₀ is a first exposure indicator; γ₉₀ is a second exposure indicator; Th1 is a predetermined contrast threshold value; and Th4 is predetermined exposure threshold value.
 8. The system of claim 5, wherein the controller outputs the image to the noise filter when:

    c>Th3;

and

    γ.sub.100 <Th4

wherein: c is the number of local area contrast variance values that are greater than the predetermined contrast threshold value; Th3 is a predetermined threshold value; and Th4 is a predetermined exposure threshold value.
 9. The system of claim 5, wherein the controller outputs the image to the noise filter when:

    c>Th3;

    γ.sub.100 <Th4;

and

    γ.sub.90 <Th4

wherein: c is the number of local area contrast variance values that are greater than the predetermined contrast threshold value; Th3 is a predetermined threshold value; and Th4 is a predetermined exposure threshold value.
 10. The system of claim 3, wherein the image processor further comprises a memory that temporarily stores the image.
 11. A scan-to-print imaging system, comprising:an image digitizer that digitizes an original image and outputs a signal representative of the image, the signal being in a first color space; and an automated image enhancement device that inputs the signal and outputs an enhanced signal, the automated image enhancement device enhancing a perceived quality of an image represented by the enhanced signal, the automated image enhancement device comprising:a noise filter outputting the enhanced signal; and an image processor that receives the signal, determines if noise in the signal corresponding to noise in the original image is above a predetermined threshold, and outputs the signal to the noise filter if the noise in the converted signal is above the predetermined threshold, wherein the predetermined threshold is set so that the automated image enhancement device outputs the enhanced signal within a predetermined response time.
 12. A scan-to-print imaging system, comprising:an image digitizer that digitizes an original image and outputs a signal representative of the image, the signal being in a first color space; and an automated image enhancement device that inputs the signal, and outputs an enhanced signal, the automated image enhancement device enhancing a perceived quality of an image represented by the enhanced signal, the automated image enhancement device comprising:a noise filter outputting the enhanced signal; an image processor that receives the signal, determines if noise in the signal corresponding to noise in the original image is above at least one predetermined threshold, and outputs the signal to the noise filter if the noise in the signal is above the at least one predetermined threshold, comprising:a histogram generator that generates from the image at least one of a global histogram and at least one local area histogram, a contrast analyzer that generates at least one contrast variance value based on the at least one of a global histogram and the at least one local area histogram, an exposure analyzer that generates at least one exposure indicator based on the at least one of a global histogram and the at least one local area histogram, and a controller that controls the histogram generator, the contrast analyzer and the exposure analyzer, wherein the at least one contrast variance value and the at least one exposure indicator are indicative of the noise in the signal.
 13. The system of claim 12, wherein the exposure analyzer generates two exposure indicators.
 14. The system of claim 13, wherein the contrast analyzer generates a global contrast variance value based on the global histogram, and at least one local area contrast variance value based on the at least one local area histogram.
 15. The system of claim 14, wherein the controller outputs the converted signal to the noise filter when:

    V.sub.G >Th1,

and

    γ.sub.100 <Th4

wherein: V_(G) is the global contrast variance value; γ₁₀₀ is an exposure indicator; Th1 is a first predetermined contrast threshold value; and Th4 is second predetermined exposure threshold value.
 16. The system of claim 14, wherein the controller outputs the converted signal to the noise filter when:

    V.sub.G >Th1;

    γ.sub.100 <Th4;

and

    γ.sub.90 <Th4

wherein: V_(G) is the global contrast variance value; γ₁₀₀ is a first exposure indicator; γ₉₀ is a second exposure indicator; Th1 is a predetermined contrast threshold value; and Th4 is predetermined exposure threshold value.
 17. The system of claim 14, wherein the controller outputs the converted signal to the noise filter when:

    c>Th3;

and

    γ.sub.100 <Th4

wherein: c is the number of local area contrast variance values that are greater than the predetermined contrast threshold value; Th3 is a predetermined threshold value; and Th4 is a predetermined exposure threshold value.
 18. The system of claim 14, wherein the controller outputs the converted signal to the noise filter when:

    c>Th3;

    γ.sub.100 <Th4;

and

    γ.sub.90 <Th4

wherein: c is the number of local area contrast variance values that are greater than the predetermined contrast threshold value; Th3 is a predetermined threshold value; and Th4 is a predetermined exposure threshold value.
 19. The system of claim 12, wherein the image processor further comprises a memory that temporarily stores the image.
 20. A method of selectively noise filtering images, comprising the steps of:determining if noise in an original image is above a corresponding predetermined threshold, comprising:generating from the image at least one of a global histogram and at least one local area histogram, generating at least one contrast variance value based on the at least one of a global histogram and the at least one local area histogram, generating at least one exposure indicator based on the at least one of a global histogram and the at least one local area histogram, comparing the at least one exposure indicator to a predetermined exposure threshold value, comparing the at least one contrast variance value to a predetermined contrast threshold value, and determining if at least one noise indicator of the at least one exposure indicator and the at least one contrast variance value is above the corresponding predetermined threshold based on results of the comparing steps; and filtering the original image if the at least one image noise indicator is above the predetermined threshold.
 21. The method of claim 20, wherein a global contrast variance value based on the global histogram, and at least one local area contrast variance value based on the at least one local area histogram are calculated.
 22. The method of claim 20, wherein two exposure indicators are generated.
 23. The method of claim 22, wherein the image is filtered when:

    V.sub.G >Th1,

and

    γ.sub.100 <Th4

wherein: V_(G) is the global contrast variance value; γ₁₀₀ is an exposure indicator; Th1 is a first predetermined contrast threshold value; and Th4 is second predetermined exposure threshold value.
 24. The method of claim 22, wherein the image is filtered when:

    V.sub.G >Th1;

    γ.sub.100 <Th4;

and

    γ.sub.90 <Th4

wherein: V_(G) is the global contrast variance value; γ₁₀₀ is a first exposure indicator; γ₉₀ is a second exposure indicator; Th1 is a predetermined contrast threshold value; and Th4 is predetermined exposure threshold value.
 25. The method of claim 22, wherein the image is filtered when:

    c>Th3;

and

    γ.sub.100 <Th4

wherein: c is the number of local area contrast variance values that are greater than the predetermined contrast threshold value; Th3 is a predetermined threshold value; and Th4 is a predetermined exposure threshold value.
 26. The method of claim 22, wherein the image is filtered when:

    c>Th3;

    γ.sub.100 <Th4;

and

    γ.sub.90 <Th4

wherein: c is the number of local area contrast variance values that are greater than the predetermined contrast threshold value; Th3 is a predetermined threshold value; and Th4 is a predetermined exposure threshold value. 